关键词: 关键词: 医学代码预测, 自动ICD编码, 层次结构, 异构信息, 自然语言处理

Abstract: Abstract: Concern of structural diversity of medical Electronic Health Records (EHRs) and the complicated correlation between coding in the automatic ICD coding task, an automatic ICD method AIC-HI (Automatic ICD Coding integrating Heterogeneous Information) that integrates heterogeneous information was proposed. Firstly, various feature extractors were designed based on the distinctive characteristics of structured coding, semi-structured description, and unstructured medical text in the coding task; At the same time, the coding knowledge graph was constructed to fit the hierarchical relationship of coding, and the association relationship between different branches was transformed into a triple containing head and tail coding; Then representation learning was used to fuse encoding and description information to calculate label features; Finally, the attention mechanism was used to extract the most relevant feature representation in unstructured documents. The experimental results show that, compared with the suboptimal baseline model MARN (Multitask bAlanced and Recalibrated Network), the microscopic F1-score of the model AIC-HI on the real clinical dataset MIMIC-III is increased by 4.3 percentage points.

Key words: Keywords: Medical Code Prediction, Automatic ICD Coding, Hierarchical Structure, Heterogeneous Information, Natural Language Processing (NLP)